2020-05-29T10:51:11ZData and code for: Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climatehttps://hdl.handle.net/2144/40340
Data and code for: Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climate
Kaufmann, Robert K.; Pretis, Felix
To test hypotheses about glacial dynamics, the Mid-Brunhes event, and the stage 11 paradox, we evaluate the ability of a statistical model to simulate climate during the previous ~800,000 years. Throughout this period, the model simulates the timing and magnitude of glacial cycles, including the saw-tooth pattern in which ice accumulates gradually and ablates rapidly, without nonlinearities or threshold effects. This suggests that nonlinearities and/or threshold effects do not play a critical role in glacial cycles. Furthermore, model accuracy throughout the previous ~800,000 years suggest that changes in glacial cycles associated with the Mid-Brunhes event, which occurs near the division between the out-of-sample period and the in-sample period, are not caused by changes in the dynamics of the climate system. Conversely, poor model performance during MIS stage 11 and Termination V is consistent with arguments that the ‘stage 11 paradox’ represents a mismatch between orbital geometry and climate. Statistical orderings of simulation errors indicate that periods of reduced accuracy start with significant reductions in the model’s ability to simulate carbon dioxide, non-sea-salt sodium, and non-sea-salt calcium. Their importance suggests that the stage 11 paradox is generated by changes in atmospheric and/or oceanic circulation that affect ocean ventilation of carbon dioxide.
These data and code can be used to reproduce the results described in the paper Testing Hypotheses About Glacial Dynamics and the Stage 11 Paradox Using a Statistical Model of Paleo-Climate that is submitted for peer review in Climate of the Past.
2020-04-01T00:00:00ZBuckling Instability Classification (BIC)https://hdl.handle.net/2144/40085
Buckling Instability Classification (BIC)
Lejeune, Emma
The Buckling Instability Classification (BIC) datasets contain the results of finite element simulations where a heterogeneous column is subject to a fixed level of applied displacement and is classified as either "Stable" or "Unstable." Each model input is a 16x1 vector where the entries of the vector dictate the Young's Modulus (E) of the corresponding portion of the physical column domain. Each input file has 16 columns one for each vector entry. For each 16x1 vector input, there is a single output that indicates if the column was stable or unstable at the fixed level of applied displacement. An output value of "0" indicates stable, and an output value of "1" indicates unstable. In BIC-1, we only allow two possible discrete values for E: E=1 or E=4. In BIC-2, we allow three possible discrete values for E: E=1, E=4, or E=7. In BIC-3, we allow continuous values (to three digits of precision) of E in the range E=1–8. BIC-1 consists of 65,536 simulation results. This exhausts the entire possible input domain. BIC-2 consists of 100,000 simulation results. This is less than 1% of the entire possible input domain. BIC-3 also consists of 100,000 simulation results. This is a tiny fraction of the entire possible input domain.
Link to the manuscript “Geometric stability classification: datasets, metamodels, and adversarial attacks” is forthcoming. All code necessary to generate the BIC datasets and reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/BIC). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2020-01-01T00:00:00ZDiagnosis Codes for Addiction and Mental Health Researchhttps://hdl.handle.net/2144/39358
Diagnosis Codes for Addiction and Mental Health Research
Hadland, Scott E.; Bagley, Sarah M.; Gai, Mam Jarra; Earlywine, Joel J.; Schoenberger, Samantha F.; Morgan, Jake R.; Barocas, Joshua A.
These diagnosis codes can be used in the study of opioid use disorder and related conditions. The spreadsheets include International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10) diagnosis codes for opioid-related complications (i.e., opioid use disorder and opioid-related overdose) and other substance use disorders, as well as comorbid mental health conditions. Sources: International Classification of Diseases, Ninth Revision (ICD-9) and Tenth Revision (ICD-10)
2020-01-01T00:00:00ZGeorinexhttps://hdl.handle.net/2144/39121
Georinex
Hirsch, Michael
RINEX 3 and RINEX 2 reader and batch conversion to NetCDF4 / HDF5 in Python or Matlab. Batch converts NAV and OBS GPS RINEX (including Hatanaka compressed OBS) data into xarray.Dataset for easy use in analysis and plotting. This gives remarkable speed vs. legacy iterative methods, and allows for HPC / out-of-core operations on massive amounts of GNSS data. GeoRinex works in Python ≥ 3.6 and has over 150unit tests driven by Pytest.
2019-03-28T00:00:00ZMechanical MNIST - Uniaxial Extensionhttps://hdl.handle.net/2144/38693
Mechanical MNIST - Uniaxial Extension
Lejeune, Emma
Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical MNIST is generated by first converting the MNIST bitmap images (http://www.pymvpa.org/datadb/mnist.html) to 2D heterogeneous blocks of material. Consistent with the MNIST bitmap ($28 \times 28$ pixels), the material domain is a $28 \times 28$ unit square. In “Mechanical MNIST - Uniaxial Extension,” the material is Neo-Hookean with a varying modulus. The bottom of the domain is fixed (Dirichlet boundary condition), the left and right edges of the domain are free, and the top of the domain is fixed horizontally and moved vertically to a set of given fixed displacements (d = [0.0, 0.001, 0.01, 0.1, 0.5, 1.0, 2.0, 4.0, 6.0, 8.0, 10.0, 12.0, 14.0 ]). The results of the simulations include: (1) change in strain energy at each step, (2) total reaction force at the top boundary at each step, and (3) full field displacement at each step. All simulations are conducted with the FEniCS computing platform (https://fenicsproject.org). The code to reproduce these simulations is hosted on GitHub (https://github.com/elejeune11/Mechanical-MNIST/tree/master/generate_dataset).
The paper "Mechanical MNIST: A benchmark dataset for mechanical metamodels" can be found at https://doi.org/10.1016/j.eml.2020.100659. All code necessary to reproduce the metamodels demonstrated in the manuscript is available on GitHub (https://github.com/elejeune11/Mechanical-MNIST). For questions, please contact Emma Lejeune (elejeune@bu.edu).
2019-12-01T00:00:00ZInteractive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)https://hdl.handle.net/2144/38691
Interactive single cell RNA-Seq analysis with Single Cell Toolkit (SCTK)
Johnson, W. Evan; Jenkins, David; Khan, Mohammed Muzamil; Faits, Tyler; Zhang, Yuqing; McFarlane, Ada; Zhao, Yue; Campbell, Joshua D.; Yajima, Masanao
I will present the Single Cell Toolkit (SCTK), an R package and interactive single cell RNA-sequencing (scRNA-Seq) analysis package that provides the first complete workflow for scRNA-Seq data analysis and visualization using a set of R functions and an interactive web interface. Users can perform analysis with modules for filtering raw results, clustering, batch correction, differential expression, pathway enrichment, and scRNA-Seq study design. The toolkit supports command line or pipeline data processing, and results can be loaded into the GUI for additional exploration and downstream analysis. We demonstrate the effectiveness of the SCTK on multiple scRNA-seq examples, including data from mucosal-associated invariant T cells, induced pluripotent stem cells, and breast cancer tumor cells. While other scRNA-Seq analysis tools exist, the SCTK is the first fully interactive analysis toolkit for scRNA-Seq data available within the R language.
2019-05-01T00:00:00ZAssociations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes - Supplemental Table 1https://hdl.handle.net/2144/38476
Associations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes - Supplemental Table 1
Lee, Sun Young; Cabral, Howard J.; Aschengrau, Ann; Pearce, Elizabeth N.
Supplemental table 1 for "Associations between Maternal Thyroid Function in Pregnancy and Obstetric and Perinatal Outcomes." Covariates included in the multivariable regression analyses.
MATLAB code and data processing guide for phase resolved Doppler Optical Coherence Tomographyhttps://hdl.handle.net/2144/37076
MATLAB code and data processing guide for phase resolved Doppler Optical Coherence Tomography
Tang, Jianbo; Erdener, Sefik Evren; Fu, Buyin; Boas, David A.
This guide and the MATLAB code are for post data processing of prDOCT, which outputs 3D vascular blood flow velocity.
Example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC
MATLAB code and data processing guide for Optical Coherence Tomography Angiographyhttps://hdl.handle.net/2144/37075
MATLAB code and data processing guide for Optical Coherence Tomography Angiography
Tang, Jianbo; Erdener, Sefik Evren; Sunul, Smrithi; Boas, David A.
This guide is for post data processing of OCTA which outputs the vascular structure.
Example data is available through:
https://drive.google.com/a/bu.edu/file/d/1q9_F93_5p_pgmXIwzKZOCQ36m7sE95d2/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1nNzjBI2JZFf4epRr4XSKhRYiT7FJcZT7/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1dAwY56dSBoRLX246ALTeV3ZiV3dehrEH/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1TUA9L170blYQdrI6L9oiSkxae7XpV--E/view?usp=sharing
https://drive.google.com/a/bu.edu/file/d/1J3gG6HFBK2uVjDgCRHbRV6fvA2Ei9YHp/view?usp=sharing
MATLAB code and data processing guide for g1OCTAhttps://hdl.handle.net/2144/37074
MATLAB code and data processing guide for g1OCTA
Tang, Jianbo; Erdener, Sefik Evren; Sunil, Smrithi; Boas, David A.
This guide is for post data processing of g1OCTA which outputs 3D vascular structure with flow
direction and minimized tail artifacts.
Example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC
MATLAB code and data processing guide for Dynamic Light Scattering-Optical Coherence Tomographyhttps://hdl.handle.net/2144/37072
MATLAB code and data processing guide for Dynamic Light Scattering-Optical Coherence Tomography
Tang, Jianbo; Erdener, Sefik Evren; Li, Baoqiang; Fu, Buyin; Sakadzic, Sava; Carp, Stefan A.; Lee, Jonghwan; Boas, David A.
This guide is for post data processing of DLSOCT, which outputs axial velocity (Vz), transverse
velocity (Vx), total velocity(V), the ratio of static component (Ms), the ratio of dynamic component (Mf), and fitting accuracy (R). The speed upper limit is determined by OCT system Aline rate and 3Dvoxel size.
An example data is available through:
https://drive.google.com/open?id=168HD4lKt0K97g09zus6H9h7lAyO0jOBZ
https://drive.google.com/open?id=1QvTO_41cPN3_wM9wxCh9NECv_hypVZPC
Sensitivity of global pasturelands to climate variationhttps://hdl.handle.net/2144/36592
Sensitivity of global pasturelands to climate variation
Stanimirova, Radost; Arévalo, Paulo; Kaufmann, Robert K.; Maus, Victor; Lesiv, Myroslava; Havlík, Petr; Friedl, Mark A.
Pasturelands are globally extensive, sensitive to climate, and support livestock production systems that provide an essential source of food in many parts of the world. In this paper, we integrate information from remote sensing, global climate, and land use databases to improve understanding of the resilience and resistance of this ecologically vulnerable and societally-critical land use. To characterize the effect of climate on pastureland productivity, we analyze the relationship between satellite-derived vegetation index and gridded precipitation datasets at 1 to 6-month time lags. To account for the effects of different production systems, we stratify our analysis by agroecological zone and by rangeland-based versus mixed crop-livestock system. Results show that 14.5% of global pasturelands experienced statistically significant greening or browning trends over the 15-year study period, with the majority of these locations showing greening. In arid ecosystems, precipitation and lagged vegetation index anomalies explain up to 69% of variation in vegetation productivity in both crop-livestock and rangeland-based production systems. Livestock production systems in Australia are least resistant to contemporaneous and short-term precipitation anomalies, while arid livestock production systems in Latin America are least resilient to short-term vegetation greenness anomalies. More generally, large swaths of semi-arid global pasturelands show substantial sensitivity to variation in precipitation, and hence, are vulnerable to climate change. Because many arid regions of the world are projected to experience decreased total precipitation and increased precipitation variability in the coming decades, improved understanding regarding the sensitivity of pasturelands to the joint effects of climate change and production system is required to support sustainable land management in global pasturelands.
2019-01-01T00:00:00ZCode distribution and example data for submission of Functional ultrasound speckle decorrelation-based velocimetry of the brainhttps://hdl.handle.net/2144/36022
Code distribution and example data for submission of Functional ultrasound speckle decorrelation-based velocimetry of the brain
Tang, Jianbo; Postnov, Dmitry; Kilic, Kivilcim; Erdener, Sefik Evren; Lee, Blaire; Szabo, Thomas L.; Boas, David A.
Supplement to "The sorted effects method: discovering heterogeneous effects beyond their averages"https://hdl.handle.net/2144/34410
Supplement to "The sorted effects method: discovering heterogeneous effects beyond their averages"
Chernozhukov, Victor; Fernández-Val, Iván; Luo, Ye
This zip file contains the replication files for the manuscript. It also contains an online appendix. The supplementary material contains 7 appendices with additional results and some omitted proofs. Appendix C introduces some notation. Appendix D includes a brief review of differential geometry. Appendix E gathers the proofs of the key mathematical results in Appendix A. Appendix F provides sufficient conditions for the u-Donsker properties in Section 4. Appendix G extends the theoretical analysis to include discrete covariates. Appendices H and I report the results of 3 numerical simulations and an empirical application to the effect of race on mortgage denials, respectively.
Accepted manuscript full text available here: https://hdl.handle.net/2144/34409
2018-11-01T00:00:00ZSupporting data in ENDNOTE for: Focused ultrasound transiently increases membrane conductance in isolated crayfish axonhttps://hdl.handle.net/2144/32721
Supporting data in ENDNOTE for: Focused ultrasound transiently increases membrane conductance in isolated crayfish axon
Lin, Jen-Wei; Yu, Feiyuan; Müller, Wolfgang S.; Ehnholm, Gösta; Okada, Yoshio
2018-11-28T00:00:00ZProject impact assessment on deforestation and forest degradation: Forest Disturbance Datasethttps://hdl.handle.net/2144/31321
Project impact assessment on deforestation and forest degradation: Forest Disturbance Dataset
Bullock, Eric; Nolte, Christoph; Reboredo Segovia, Ana
Guatemala has 259 designated protected areas covering approximately 32% of its land (or around 35,000 km2). From 2012-2017, the Inter-American Development Bank (IDB) and Guatemala invested in the mapping and demarcation of land boundaries related to the nation’s protected areas. The goal of our project was to investigate the effectiveness of the program in reducing forest disturbances within the protected areas. Deforestation and degradation were mapped at 30m resolution using Landsat data and the Continuous Degradation Detected (CODED) algorithm on the Google Earth Engine. The study time period was January 1, 1999 through December 31, 2017. The results are a mapped dataset with information on dates of deforestation and degradation events for the Republic of Guatemala. The file can be opened in any software intended for geospatial analysis such as QGIS, ArcGIS, or ENVI.
AUTHORS: Eric Bullock, Ana Reboredo Segovia, and Christoph Nolte
COLLABORATING INSTITUTIONS: Inter-American Development Bank (IDB), Boston University, Registro de Información Catastral, and Consejo Nacional de Areas Protegidas (CONAP)
CONTACT: bullocke@bu.edu
START DATE: 1999-01-01
END DATE: 2017-12-31
SPATIAL RESOLUTION: 30m
DATASOURCE: Landsat 4, 5, 7, and 8
ALGORITHM: Continuous Degradation Detection (CODED), see: github.com/bullocke/coded
FORMAT: GeoTIFF
DIMENSIONS: 14968, 15127
COMPRESSION: LZW
INTERLEAVE: Band
PROJECTION: EPSG:4326 WGS 84
** BAND INFORMATION AND PIXEL VALUES
BAND 1: Map Strata
1. Stable forest
2. Not stable forest
7. Degradation
8. Deforestation
BAND 2: First deforestation date
1999-2017: Disturbance date
BAND 3: First degradation date
1999-2017: Disturbance date
** VALIDATION AND ESTIMATION
SAMPLING PROCEDURE: Stratified Random Samples
SAMPLING AND ANALYSIS TOOLS: github.com/bullocke/gee-assessment-toolbox
REFERENCE DATA: Google Earth imagery and Landsat historical data
STRATA AND SAMPLES
1. Forest 400
2. Non Forest 300
3. Degradation 150
4. Deforestation 150
5. Buffer 100
AREA ESTIMATES AND STANDARD ERRORS (HECTARES)
1. Forest 4860711.641 (104561.4505)
2. Non Forest 4702034.137 (90917.05746)
3. Deforestation 859800.9247 (39926.73148)
4. Degradation 958812.9767 (68792.98674)
ACCURACIES
Overall Accuracy: 87.6%
Producers:
Forest: 95.8%
Non-Forest: 86.7%
Deforestation: 85.6%
Degradation: 52.9%
Users:
Forest: 82.3%
Non-Forest: 95.6%
Deforestation: 85.7%
Degradation: 83.9%
2018-08-31T00:00:00ZPopCluster: an algorithm to identify genetic variants with ethnicity-dependent effectshttps://hdl.handle.net/2144/29809
PopCluster: an algorithm to identify genetic variants with ethnicity-dependent effects
Gurinovich, Anastasia
Dataset for PopCluster: a new algorithm to identify genetic variants with ethnicity-dependent effects. See also github repository for this project: https://github.com/gurinovich/PopCluster
2018-07-11T00:00:00ZChanging the South African national antiretroviral therapy guidelines: The role of cost modellinghttps://hdl.handle.net/2144/24129
Changing the South African national antiretroviral therapy guidelines: The role of cost modelling
Meyer-Rath, Gesine; Johnson, Leigh F.; Pillay, Yogan; Blecher, Mark; Brennan, Alana T.; Long, Lawrence; Moultrie, Harry; Sanne, Ian; Fox, Matthew P.; Rosen, Sydney
Background We were tasked by the South African Department of Health to assess the cost implications to the largest ART programme in the world of adopting sets of ART guidelines issued by the World Health Organization between 2010 to 2016.
Methods Using data from large South African ART clinics (n = 24,244 patients), projections of patients in need of ART, and cost data from bottom-up cost analyses, we constructed a population-level health-state transition model with 6-monthly transitions between health states depending on patients’ age, CD4 cell count/ percentage, and, for adult first-line ART, time on treatment.
Findings For each set of guidelines, the modelled increase in patient numbers as a result of prevalence and uptake was substantially more than the increase resulting from additional eligibility. Under each set of guidelines, the number of people on ART was projected to increase by 31-133% over the next seven years, and cost by 84-175%, while increased eligibility led to 1-26% more patients, and 1-17% higher cost. The projected increases in treatment cost due to the 2010 and the 2015 WHO guidelines could be offset in their entirety by the introduction of cost-saving measures such as opening the drug tenders for international competition and task-shifting. Under universal treatment, annual costs of the treatment programme will decrease for the first time from 2024 onwards.
Conclusions Annual budgetary requirements for ART will continue to increase in South Africa until universal treatment is taken to full scale. Model results were instrumental in changing South African ART guidelines, more than tripling the population on treatment between 2009 and 2017, and reducing the per-patient cost of treatment by 64%.
Boston University Libraries 2016 Undergraduate Survey Datasethttps://hdl.handle.net/2144/22299
Boston University Libraries 2016 Undergraduate Survey Dataset
Boston University Libraries Assessment Committee
In the spring of 2016, the Boston University libraries surveyed BU faculty, graduate students, and undergraduate students to determine their use of and satisfaction with library services and resources. This dataset contains the results of the undergraduate responses, questions used in the survey, and normalization tables for analysis. The data was stripped of qualitative data to ensure anonymity. A report describing the protocol and analyzing the data gathered in this survey may be found at this location, http://hdl.handle.net/2144/20325.
Boston University Libraries 2016 Graduate Survey Datasethttps://hdl.handle.net/2144/22298
Boston University Libraries 2016 Graduate Survey Dataset
Boston University Libraries Assessment Committee
In the spring of 2016, the Boston University libraries surveyed BU faculty, graduate students, and undergraduate students to determine their use of and satisfaction with library services and resources. This dataset contains the results of the graduate student responses, questions used in the survey, and normalization tables for analysis. The data was stripped of qualitative data to ensure anonymity. A report describing the protocol and analyzing the data gathered in this survey may be found at this location, http://hdl.handle.net/2144/20325.